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Development of a Clinical Prediction Model for Infective Endocarditis Among Patients with Undiagnosed Fever: A Pilot
Shun Yamashita1, Masaki Tago1, So Motomura1
1Department of General Medicine, Saga University Hospital, Saga, Japan.
Purpose:
Infective endocarditis (IE) may be diagnosed as fever of unknown origin due to its delusively non-descriptive clinical features, especially in outpatient clinics. Our objective is to develop a prediction model to discriminate patients to be diagnosed as "definite" IE from "non-definite" by modified Duke criteria among patients with undiagnosed fever, using only history and results of physical examinations and common laboratory examinations.
Patients And Methods:
The study was a single-center case-control study. Inpatients at Saga University Hospital diagnosed with IE from 2007 to 2017 and patients with undiagnosed fever from 2015 to 2017 were enrolled. Patients diagnosed with definite IE according to the modified Duke criteria, except those definitely diagnosed with other disorders responsible for fever, were allocated to the IE group. Patients without IE among those defined as non-definite according to the modified Duke criteria were allocated to the undiagnosed fever group. We developed a prediction model to pick up patients who would be "definite" by modified Duke criteria, which was subsequently assessed by area under the curve (AUC).
Results:
A total of 144 adult patients were included. Of these, 59 patients comprised the IE group. We developed the prediction model using five indicators, including transfer by ambulance, cardiac murmur, pleural effusion, neutrophil count, and platelet count, with a sensitivity 84.7%, a specificity 84.7%, an AUC 0.893 (95% confidence interval 0.828-0.959), a shrinkage coefficient 0.635, and a stratum-specific likelihood ratio 0.2-50.4.
Conclusion:
Our prediction model, which uses only indicators easy to gain, facilitates prediction of patients with IE. These indicators can be acquired even at common hospitals and clinics, without requiring advanced medical equipment or invasive examinations.
Trial Registration Number:
UMIN000041344.
Insights
A new prediction model aids in diagnosing infective endocarditis (IE) in fever of unknown origin patients. This model uses simple clinical and lab indicators, improving early detection in various healthcare settings.
Area of Science:
- Cardiology
- Infectious Diseases
- Clinical Prediction Modeling
Background:
- Infective endocarditis (IE) often presents with non-specific symptoms, mimicking fever of unknown origin (FUO).
- Accurate diagnosis of IE can be challenging, particularly in outpatient settings, due to its subtle clinical manifestations.
Purpose of the Study:
- To develop a prediction model for discriminating definite infective endocarditis (IE) from non-definite IE in patients with undiagnosed fever.
- The model aims to utilize readily available clinical and laboratory data for improved diagnostic accuracy.
Main Methods:
- A single-center case-control study involving 144 adult patients diagnosed with IE or undiagnosed fever.
- Patients were categorized based on modified Duke criteria.
- A prediction model was developed and validated using area under the curve (AUC).
Main Results:
- The developed prediction model incorporated five indicators: ambulance transfer, cardiac murmur, pleural effusion, neutrophil count, and platelet count.
- The model achieved a sensitivity of 84.7%, specificity of 84.7%, and an AUC of 0.893.
- The model demonstrated good discriminative ability for identifying definite IE.
Conclusions:
- The developed prediction model effectively identifies patients with infective endocarditis using easily obtainable indicators.
- These indicators are accessible even in primary care settings, requiring no advanced equipment or invasive procedures.
- The model facilitates earlier and more accurate diagnosis of IE, potentially improving patient outcomes.
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